The MEG Core staff works interactively with an extensive group of PI's in NIMH, NINDS and NIDCD for study design, task programming development, acquisition protocols, and signal processing and data analysis. Procedures have been setup for data security, transfer and storage. We have also worked with the Scientific and Statistical Computing Core to enable transfer of CTF MEG files to AFNI and developed tools for group statistical analysis. The instant correlation feature of AFNI is being used to investigate connectivity pattern in MEG data. Other features of AFNI that allow extraction of brain surface meshes are being used to allow more detailed modeling of the MEG source signal. Technical and scientific results continue to be excellent. Signal analysis development includes event-related SAM (synthetic aperture magnetometry) and 275 channel ICA (independent component analysis). Development of time-frequency analysis methods has included Stockwell and wavelet transforms as well as multi-taper techniques. The SAM software can be run on the Biowulf Cluster (utilizing the open source OCTAVE software installed by MEG Core staff) allowing for tremendous increase in computing power. 2012 marked the tenth anniversary of the MEG Core. Although installed over ten years ago our CTF designed MEG system in many ways remains state of the art especially in terms of the front end sensor design. However, the much of the electronics use aged components. The external electronic rack has been redesigned to utilize modern circuitry and computer systems. We are planning installation of this upgrade in fall 2013. This will substantially improve reliability and substantially increase the useful life of the system. The ability to localize not only cortical surface sources but deeper structures has been demonstrated. For a working memory task MEG activation patterns for beta band have shown exceptional agreement with fMRI (functional magnetic resonance imaging) results in the same subject group. Beta desynchronization patterns agree highly with the network of bilateral DLPFC (dorsolateral prefrontal cortex) and posterior parietal cortex seen during working memory in fMRI tasks. These results are now being compared across modalities and signal measures to better understand the individual patterns of brain activation and their behavioral correlates. In earlier work Brian Cornwell and colleagues have demonstrated that MEG can reliably discriminate amygdala and hippocampal signals using MEG beamforming techniques. Continuing studies have shown that hippocampal function is impaired in patients with major depression as well as other brain changes when treated with ketamine. These studies are of particular interest to possibly elucidate the mechanism of the anti-depressant action of ketamine infusion. Previous results have shown that both increased anterior cingulate activity and functional connectivity during a working memory task can predict the antidepressant response of ketamine. Zarate and colleagues have utilized MEG to show that synaptic potentiation is critical for the antidepressant action in treat resistant major depression. Studying how the brain organizes itself into functional networks is key to understanding normal human cognition as well as when it becomes disordered in mental illness. Previously, Bassett and co-workers using the spatial and temporal ability of MEG to see changes of configuration during a task, found that functional networks were characterized by small-world properties indicating a mix of both local connections and long range connections. They extended this work to demonstrate that dysfuctional networks can be detected and related to behavioral differences in clinical groups. We have also found differences in resting network patterns in patient groups, and have now used graph theoretical methods to examine functional networks. The interest in 'resting activity' has continued with Dr Biyu He investigating scale free properties in functional networks. Ghuman and Martin have shown that synchronous networks have similar patterns during both spontaneous recording and when stimulus driven. Shriki and Plenz have shown neuronal avalanches are found in resting MEG recordings, indicating critical scale-free dynamics. This will be extended to explore differences in a clinical population. Previously Dr Horwitz and his NIDCD group have extended large scale neural models to examine connectivity measures that can reflect cortical dynamics at millisecond resolution and have now shown that early sensory cortex is activated during cross-modal retrieval. Dr Braun's group has used MEG to examine patterns of syntactic comprehension in language processing and is now preparing to compare MEG to eCOG recordings.